Semi-Supervised Vector Quantization for proximity data
نویسندگان
چکیده
Semi-supervised learning (SSL) is focused on learning from labeled and unlabeled data by incorporating structural and statistical information of the available unlabeled data. The amount of data is dramatically increasing, but few of them are fully labeled, due to cost and time constraints. This is even more challenging for non-vectorial, proximity data, given by pairwise proximity values. Only few methods provide SSL for this data, limited to positive-semi-definite (psd) data. They also lack interpretable models, which is a relevant aspect in life-sciences where most of these data are found. This paper provides a prototype based SSL approach for proximity data.
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